paragraph40.75ex plus1ex minus.2ex-1emDenseCL with DINO init.
For further comparison to our closest competitor in transfer learning, DenseCL [1]}, we trained a ViT with DINO initalization using their loss and following the setting of Tab. 3 for PVOC12.
We find a performance of 54% and 17.1% for LC and K=500 evaluation respectively, fine-tuning with Leopart still outperforms by \(>\!\!15\%\)  for LC and \(>\!\!40\%\)  for K=500.
These results indicate that DenseCL (perhaps due to its global-pooled loss term) does not seem apt for fine-tuning as it barely improves upon the DINO initialisation (\(+3.4\%\)  for LC and \(-0.3\%\)  for K=500).
